Overview

Dataset statistics

Number of variables17
Number of observations50
Missing cells6
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory138.6 B

Variable types

Numeric14
Categorical3

Alerts

August is highly overall correlated with Total_salesHigh correlation
May is highly overall correlated with Total_salesHigh correlation
November is highly overall correlated with Total_salesHigh correlation
Price is highly overall correlated with Product NameHigh correlation
Product Name is highly overall correlated with PriceHigh correlation
Total_sales is highly overall correlated with August and 2 other fieldsHigh correlation
March has 1 (2.0%) missing valuesMissing
April has 1 (2.0%) missing valuesMissing
August has 1 (2.0%) missing valuesMissing
Total_sales has 3 (6.0%) missing valuesMissing
ID is uniformly distributedUniform
Product Name is uniformly distributedUniform
ID has unique valuesUnique
September has unique valuesUnique

Reproduction

Analysis started2024-02-19 17:53:47.972454
Analysis finished2024-02-19 17:54:19.231644
Duration31.26 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:19.391473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.45
Q113.25
median25.5
Q337.75
95-th percentile47.55
Maximum50
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57738
Coefficient of variation (CV)0.57166195
Kurtosis-1.2
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum1275
Variance212.5
MonotonicityStrictly increasing
2024-02-19T21:54:19.609681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
38 1
 
2.0%
28 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Last,First
Categorical

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Brennan, Micheal
Albertson, Kathy
Allenson, Carol
Altman, Zoey
Bittiman, William
Other values (6)
22 

Length

Max length19
Median length16.5
Mean length15
Min length11

Characters and Unicode

Total characters750
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbertson, Kathy
2nd rowAllenson, Carol
3rd rowAltman, Zoey
4th rowBittiman, William
5th rowBrennan, Micheal

Common Values

ValueCountFrequency (%)
Brennan, Micheal 7
14.0%
Albertson, Kathy 6
12.0%
Allenson, Carol 5
10.0%
Altman, Zoey 5
10.0%
Bittiman, William 5
10.0%
David, Chloe 5
10.0%
Flores, Tia 4
8.0%
Jameson, Robinson 4
8.0%
Ferguson, Elizabeth 3
6.0%
Davis, William 3
6.0%

Length

2024-02-19T21:54:19.795188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
william 8
 
8.0%
brennan 7
 
7.0%
micheal 7
 
7.0%
albertson 6
 
6.0%
kathy 6
 
6.0%
elizabeth 6
 
6.0%
bittiman 5
 
5.0%
chloe 5
 
5.0%
david 5
 
5.0%
zoey 5
 
5.0%
Other values (10) 40
40.0%

Most occurring characters

ValueCountFrequency (%)
n 65
 
8.7%
a 65
 
8.7%
l 64
 
8.5%
i 55
 
7.3%
e 52
 
6.9%
, 50
 
6.7%
50
 
6.7%
o 48
 
6.4%
t 36
 
4.8%
s 32
 
4.3%
Other values (24) 233
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 550
73.3%
Uppercase Letter 100
 
13.3%
Other Punctuation 50
 
6.7%
Space Separator 50
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 65
11.8%
a 65
11.8%
l 64
11.6%
i 55
10.0%
e 52
9.5%
o 48
8.7%
t 36
6.5%
s 32
 
5.8%
r 25
 
4.5%
h 24
 
4.4%
Other values (9) 84
15.3%
Uppercase Letter
ValueCountFrequency (%)
A 16
16.0%
C 13
13.0%
B 12
12.0%
W 8
8.0%
D 8
8.0%
F 7
7.0%
M 7
7.0%
E 6
 
6.0%
K 6
 
6.0%
Z 5
 
5.0%
Other values (3) 12
12.0%
Other Punctuation
ValueCountFrequency (%)
, 50
100.0%
Space Separator
ValueCountFrequency (%)
50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 650
86.7%
Common 100
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 65
 
10.0%
a 65
 
10.0%
l 64
 
9.8%
i 55
 
8.5%
e 52
 
8.0%
o 48
 
7.4%
t 36
 
5.5%
s 32
 
4.9%
r 25
 
3.8%
h 24
 
3.7%
Other values (22) 184
28.3%
Common
ValueCountFrequency (%)
, 50
50.0%
50
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 65
 
8.7%
a 65
 
8.7%
l 64
 
8.5%
i 55
 
7.3%
e 52
 
6.9%
, 50
 
6.7%
50
 
6.7%
o 48
 
6.4%
t 36
 
4.8%
s 32
 
4.3%
Other values (24) 233
31.1%

Product Name
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Laptop
10 
Camera
10 
Computer
10 
Mobile
10 
Calculator
10 

Length

Max length10
Median length6
Mean length7.2
Min length6

Characters and Unicode

Total characters360
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop
2nd rowCamera
3rd rowComputer
4th rowMobile
5th rowCalculator

Common Values

ValueCountFrequency (%)
Laptop 10
20.0%
Camera 10
20.0%
Computer 10
20.0%
Mobile 10
20.0%
Calculator 10
20.0%

Length

2024-02-19T21:54:19.963589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T21:54:20.147550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
laptop 10
20.0%
camera 10
20.0%
computer 10
20.0%
mobile 10
20.0%
calculator 10
20.0%

Most occurring characters

ValueCountFrequency (%)
a 50
13.9%
o 40
11.1%
p 30
8.3%
t 30
8.3%
C 30
8.3%
e 30
8.3%
r 30
8.3%
l 30
8.3%
m 20
 
5.6%
u 20
 
5.6%
Other values (5) 50
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 310
86.1%
Uppercase Letter 50
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 50
16.1%
o 40
12.9%
p 30
9.7%
t 30
9.7%
e 30
9.7%
r 30
9.7%
l 30
9.7%
m 20
 
6.5%
u 20
 
6.5%
b 10
 
3.2%
Other values (2) 20
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
C 30
60.0%
L 10
 
20.0%
M 10
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 50
13.9%
o 40
11.1%
p 30
8.3%
t 30
8.3%
C 30
8.3%
e 30
8.3%
r 30
8.3%
l 30
8.3%
m 20
 
5.6%
u 20
 
5.6%
Other values (5) 50
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 50
13.9%
o 40
11.1%
p 30
8.3%
t 30
8.3%
C 30
8.3%
e 30
8.3%
r 30
8.3%
l 30
8.3%
m 20
 
5.6%
u 20
 
5.6%
Other values (5) 50
13.9%

Price
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
aed3000
20 
aed2000
10 
aed1000
10 
aed500
10 

Length

Max length7
Median length7
Mean length6.8
Min length6

Characters and Unicode

Total characters340
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaed2000
2nd rowaed3000
3rd rowaed3000
4th rowaed1000
5th rowaed500

Common Values

ValueCountFrequency (%)
aed3000 20
40.0%
aed2000 10
20.0%
aed1000 10
20.0%
aed500 10
20.0%

Length

2024-02-19T21:54:20.360333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-19T21:54:20.514845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
aed3000 20
40.0%
aed2000 10
20.0%
aed1000 10
20.0%
aed500 10
20.0%

Most occurring characters

ValueCountFrequency (%)
0 140
41.2%
a 50
 
14.7%
e 50
 
14.7%
d 50
 
14.7%
3 20
 
5.9%
2 10
 
2.9%
1 10
 
2.9%
5 10
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190
55.9%
Lowercase Letter 150
44.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 140
73.7%
3 20
 
10.5%
2 10
 
5.3%
1 10
 
5.3%
5 10
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
a 50
33.3%
e 50
33.3%
d 50
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 190
55.9%
Latin 150
44.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 140
73.7%
3 20
 
10.5%
2 10
 
5.3%
1 10
 
5.3%
5 10
 
5.3%
Latin
ValueCountFrequency (%)
a 50
33.3%
e 50
33.3%
d 50
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 140
41.2%
a 50
 
14.7%
e 50
 
14.7%
d 50
 
14.7%
3 20
 
5.9%
2 10
 
2.9%
1 10
 
2.9%
5 10
 
2.9%

January
Real number (ℝ)

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6018
Minimum1600
Maximum16000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:20.953904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1600
5-th percentile1790
Q13892.25
median5850
Q38563
95-th percentile9736.15
Maximum16000
Range14400
Interquartile range (IQR)4670.75

Descriptive statistics

Standard deviation2976.5299
Coefficient of variation (CV)0.4946045
Kurtosis0.91930844
Mean6018
Median Absolute Deviation (MAD)2248
Skewness0.5993054
Sum300900
Variance8859730
MonotonicityNot monotonic
2024-02-19T21:54:21.146960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4300 2
 
4.0%
1600 2
 
4.0%
6000 2
 
4.0%
8563 2
 
4.0%
2354 2
 
4.0%
1900 2
 
4.0%
3799 1
 
2.0%
9787 1
 
2.0%
6379 1
 
2.0%
5745 1
 
2.0%
Other values (34) 34
68.0%
ValueCountFrequency (%)
1600 2
4.0%
1700 1
2.0%
1900 2
4.0%
2354 2
4.0%
2355 1
2.0%
2563 1
2.0%
2578 1
2.0%
3654 1
2.0%
3799 1
2.0%
3890 1
2.0%
ValueCountFrequency (%)
16000 1
2.0%
10000 1
2.0%
9787 1
2.0%
9674 1
2.0%
9500 1
2.0%
9300 1
2.0%
9077 1
2.0%
9050 1
2.0%
8900 1
2.0%
8657 1
2.0%

February
Real number (ℝ)

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5867.16
Minimum300
Maximum15389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:21.312819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1484.25
Q14065
median5755
Q37533.25
95-th percentile9858.65
Maximum15389
Range15089
Interquartile range (IQR)3468.25

Descriptive statistics

Standard deviation2813.9149
Coefficient of variation (CV)0.47960426
Kurtosis1.5045395
Mean5867.16
Median Absolute Deviation (MAD)1777.5
Skewness0.5655002
Sum293358
Variance7918117.3
MonotonicityNot monotonic
2024-02-19T21:54:21.528116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
5479 2
 
4.0%
3500 2
 
4.0%
6020 2
 
4.0%
7534 2
 
4.0%
6153 1
 
2.0%
1784 1
 
2.0%
5000 1
 
2.0%
4200 1
 
2.0%
8674 1
 
2.0%
4577 1
 
2.0%
Other values (36) 36
72.0%
ValueCountFrequency (%)
300 1
2.0%
600 1
2.0%
1239 1
2.0%
1784 1
2.0%
2319 1
2.0%
2352 1
2.0%
3092 1
2.0%
3435 1
2.0%
3465 1
2.0%
3500 2
4.0%
ValueCountFrequency (%)
15389 1
2.0%
10600 1
2.0%
9878 1
2.0%
9835 1
2.0%
9639 1
2.0%
9018 1
2.0%
8674 1
2.0%
8533 1
2.0%
8192 1
2.0%
7967 1
2.0%

March
Real number (ℝ)

MISSING 

Distinct49
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean5402.2857
Minimum1632
Maximum13531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:21.882576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1632
5-th percentile2055.2
Q13400
median4685
Q36754
95-th percentile9801
Maximum13531
Range11899
Interquartile range (IQR)3354

Descriptive statistics

Standard deviation2680.7269
Coefficient of variation (CV)0.49622087
Kurtosis0.29966143
Mean5402.2857
Median Absolute Deviation (MAD)1830
Skewness0.77344572
Sum264712
Variance7186296.8
MonotonicityNot monotonic
2024-02-19T21:54:22.108783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3400 1
 
2.0%
2342 1
 
2.0%
6413 1
 
2.0%
4831 1
 
2.0%
8353 1
 
2.0%
5854 1
 
2.0%
3257 1
 
2.0%
4535 1
 
2.0%
2345 1
 
2.0%
2344 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
1632 1
2.0%
1685 1
2.0%
1864 1
2.0%
2342 1
2.0%
2344 1
2.0%
2345 1
2.0%
2347 1
2.0%
2477 1
2.0%
2648 1
2.0%
2937 1
2.0%
ValueCountFrequency (%)
13531 1
2.0%
9879 1
2.0%
9845 1
2.0%
9735 1
2.0%
9676 1
2.0%
9347 1
2.0%
8732 1
2.0%
8616 1
2.0%
8353 1
2.0%
7657 1
2.0%

April
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)79.6%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1536.6122
Minimum1006
Maximum2335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:22.324457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1006
5-th percentile1228.4
Q11336
median1468
Q31646
95-th percentile1977
Maximum2335
Range1329
Interquartile range (IQR)310

Descriptive statistics

Standard deviation278.0932
Coefficient of variation (CV)0.18097812
Kurtosis0.14712589
Mean1536.6122
Median Absolute Deviation (MAD)144
Skewness0.77393666
Sum75294
Variance77335.826
MonotonicityNot monotonic
2024-02-19T21:54:22.590695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1468 3
 
6.0%
1546 3
 
6.0%
1235 3
 
6.0%
1336 2
 
4.0%
1462 2
 
4.0%
1964 2
 
4.0%
1369 2
 
4.0%
1867 1
 
2.0%
1534 1
 
2.0%
1968 1
 
2.0%
Other values (29) 29
58.0%
ValueCountFrequency (%)
1006 1
 
2.0%
1200 1
 
2.0%
1224 1
 
2.0%
1235 3
6.0%
1253 1
 
2.0%
1256 1
 
2.0%
1265 1
 
2.0%
1324 1
 
2.0%
1326 1
 
2.0%
1328 1
 
2.0%
ValueCountFrequency (%)
2335 1
2.0%
1988 1
2.0%
1979 1
2.0%
1974 1
2.0%
1968 1
2.0%
1964 2
4.0%
1943 1
2.0%
1895 1
2.0%
1867 1
2.0%
1846 1
2.0%

May
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7416.6
Minimum2548
Maximum19465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:22.789060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2548
5-th percentile2955.5
Q14629.25
median7374
Q39758.75
95-th percentile13034.15
Maximum19465
Range16917
Interquartile range (IQR)5129.5

Descriptive statistics

Standard deviation3579.1367
Coefficient of variation (CV)0.48258457
Kurtosis1.2266812
Mean7416.6
Median Absolute Deviation (MAD)2568
Skewness0.92887665
Sum370830
Variance12810220
MonotonicityNot monotonic
2024-02-19T21:54:23.024251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
4759 2
 
4.0%
3734 2
 
4.0%
5837 1
 
2.0%
3973 1
 
2.0%
11384 1
 
2.0%
5369 1
 
2.0%
13076 1
 
2.0%
9642 1
 
2.0%
7445 1
 
2.0%
9844 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
2548 1
2.0%
2732 1
2.0%
2753 1
2.0%
3203 1
2.0%
3235 1
2.0%
3458 1
2.0%
3465 1
2.0%
3718 1
2.0%
3734 2
4.0%
3973 1
2.0%
ValueCountFrequency (%)
19465 1
2.0%
14932 1
2.0%
13076 1
2.0%
12983 1
2.0%
12394 1
2.0%
11394 1
2.0%
11384 1
2.0%
10378 1
2.0%
10347 1
2.0%
10333 1
2.0%

June
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15218.28
Minimum10382
Maximum20473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:23.191128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10382
5-th percentile10978.8
Q112782.5
median15143.5
Q317807.5
95-th percentile19502.7
Maximum20473
Range10091
Interquartile range (IQR)5025

Descriptive statistics

Standard deviation3005.8097
Coefficient of variation (CV)0.1975131
Kurtosis-1.3249459
Mean15218.28
Median Absolute Deviation (MAD)2451
Skewness0.10406838
Sum760914
Variance9034892.2
MonotonicityNot monotonic
2024-02-19T21:54:23.380210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
19374 2
 
4.0%
16252 1
 
2.0%
16534 1
 
2.0%
10923 1
 
2.0%
13644 1
 
2.0%
15392 1
 
2.0%
17385 1
 
2.0%
13567 1
 
2.0%
15756 1
 
2.0%
17894 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
10382 1
2.0%
10500 1
2.0%
10923 1
2.0%
11047 1
2.0%
11193 1
2.0%
11273 1
2.0%
11283 1
2.0%
12122 1
2.0%
12232 1
2.0%
12320 1
2.0%
ValueCountFrequency (%)
20473 1
2.0%
19676 1
2.0%
19608 1
2.0%
19374 2
4.0%
19283 1
2.0%
19263 1
2.0%
19065 1
2.0%
18930 1
2.0%
18774 1
2.0%
18657 1
2.0%

July
Real number (ℝ)

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1602.86
Minimum1002
Maximum1987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:23.621311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1161.4
Q11375
median1646
Q31851.5
95-th percentile1980.3
Maximum1987
Range985
Interquartile range (IQR)476.5

Descriptive statistics

Standard deviation271.43181
Coefficient of variation (CV)0.16934218
Kurtosis-0.74783226
Mean1602.86
Median Absolute Deviation (MAD)218
Skewness-0.32843696
Sum80143
Variance73675.225
MonotonicityNot monotonic
2024-02-19T21:54:23.787659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1983 2
 
4.0%
1543 2
 
4.0%
1939 2
 
4.0%
1356 2
 
4.0%
1864 2
 
4.0%
1847 1
 
2.0%
1836 1
 
2.0%
1378 1
 
2.0%
1283 1
 
2.0%
1876 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
1002 1
2.0%
1003 1
2.0%
1093 1
2.0%
1245 1
2.0%
1266 1
2.0%
1283 1
2.0%
1326 1
2.0%
1329 1
2.0%
1345 1
2.0%
1355 1
2.0%
ValueCountFrequency (%)
1987 1
2.0%
1983 2
4.0%
1977 1
2.0%
1967 1
2.0%
1939 2
4.0%
1937 1
2.0%
1876 1
2.0%
1874 1
2.0%
1864 2
4.0%
1853 1
2.0%

August
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)98.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean5564.4694
Minimum1827
Maximum9765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:23.945856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1827
5-th percentile2332.4
Q13478
median4826
Q37883
95-th percentile9660.2
Maximum9765
Range7938
Interquartile range (IQR)4405

Descriptive statistics

Standard deviation2548.4834
Coefficient of variation (CV)0.45799217
Kurtosis-1.3332644
Mean5564.4694
Median Absolute Deviation (MAD)1981
Skewness0.32570602
Sum272659
Variance6494767.7
MonotonicityNot monotonic
2024-02-19T21:54:24.143110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2728 2
 
4.0%
1827 1
 
2.0%
5539 1
 
2.0%
3874 1
 
2.0%
2845 1
 
2.0%
3246 1
 
2.0%
9765 1
 
2.0%
6346 1
 
2.0%
3478 1
 
2.0%
8567 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
1827 1
2.0%
2284 1
2.0%
2324 1
2.0%
2345 1
2.0%
2565 1
2.0%
2728 2
4.0%
2732 1
2.0%
2845 1
2.0%
2849 1
2.0%
3246 1
2.0%
ValueCountFrequency (%)
9765 1
2.0%
9763 1
2.0%
9743 1
2.0%
9536 1
2.0%
9462 1
2.0%
9365 1
2.0%
8992 1
2.0%
8765 1
2.0%
8623 1
2.0%
8567 1
2.0%

September
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8035.06
Minimum3346
Maximum17955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:24.321546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3346
5-th percentile3605.25
Q15625.25
median7897
Q39735.25
95-th percentile14292.2
Maximum17955
Range14609
Interquartile range (IQR)4110

Descriptive statistics

Standard deviation3154.2032
Coefficient of variation (CV)0.39255503
Kurtosis1.0800136
Mean8035.06
Median Absolute Deviation (MAD)2067.5
Skewness0.82464836
Sum401753
Variance9948997.9
MonotonicityNot monotonic
2024-02-19T21:54:24.484764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10383 1
 
2.0%
9876 1
 
2.0%
7664 1
 
2.0%
3652 1
 
2.0%
4853 1
 
2.0%
7840 1
 
2.0%
9569 1
 
2.0%
10044 1
 
2.0%
9673 1
 
2.0%
8675 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
3346 1
2.0%
3456 1
2.0%
3567 1
2.0%
3652 1
2.0%
4028 1
2.0%
4567 1
2.0%
4729 1
2.0%
4754 1
2.0%
4828 1
2.0%
4842 1
2.0%
ValueCountFrequency (%)
17955 1
2.0%
15003 1
2.0%
14339 1
2.0%
14235 1
2.0%
11457 1
2.0%
10789 1
2.0%
10785 1
2.0%
10399 1
2.0%
10383 1
2.0%
10044 1
2.0%

October
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6803.14
Minimum1345
Maximum17443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:24.682530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1345
5-th percentile2450.4
Q13384
median6443
Q38760.5
95-th percentile13200.5
Maximum17443
Range16098
Interquartile range (IQR)5376.5

Descriptive statistics

Standard deviation3749.9527
Coefficient of variation (CV)0.55120911
Kurtosis0.24064708
Mean6803.14
Median Absolute Deviation (MAD)3032
Skewness0.72507136
Sum340157
Variance14062146
MonotonicityNot monotonic
2024-02-19T21:54:24.882974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3246 2
 
4.0%
9847 1
 
2.0%
4763 1
 
2.0%
8567 1
 
2.0%
4678 1
 
2.0%
9578 1
 
2.0%
3357 1
 
2.0%
6538 1
 
2.0%
3258 1
 
2.0%
6423 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
1345 1
2.0%
1993 1
2.0%
2445 1
2.0%
2457 1
2.0%
2458 1
2.0%
2545 1
2.0%
2578 1
2.0%
2597 1
2.0%
2857 1
2.0%
3246 2
4.0%
ValueCountFrequency (%)
17443 1
2.0%
15734 1
2.0%
13466 1
2.0%
12876 1
2.0%
12664 1
2.0%
11266 1
2.0%
10433 1
2.0%
9847 1
2.0%
9646 1
2.0%
9644 1
2.0%

November
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5644.28
Minimum857
Maximum9957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:25.082276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum857
5-th percentile1584.35
Q13282
median5000
Q38547
95-th percentile9767.05
Maximum9957
Range9100
Interquartile range (IQR)5265

Descriptive statistics

Standard deviation2797.3588
Coefficient of variation (CV)0.49560951
Kurtosis-1.3446371
Mean5644.28
Median Absolute Deviation (MAD)2473.5
Skewness0.073524967
Sum282214
Variance7825216.5
MonotonicityNot monotonic
2024-02-19T21:54:25.269010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
5347 2
 
4.0%
4643 2
 
4.0%
7345 1
 
2.0%
8643 1
 
2.0%
7647 1
 
2.0%
2478 1
 
2.0%
8575 1
 
2.0%
4987 1
 
2.0%
2322 1
 
2.0%
1043 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
857 1
2.0%
990 1
2.0%
1043 1
2.0%
2246 1
2.0%
2322 1
2.0%
2478 1
2.0%
2486 1
2.0%
2567 1
2.0%
2675 1
2.0%
2765 1
2.0%
ValueCountFrequency (%)
9957 1
2.0%
9867 1
2.0%
9835 1
2.0%
9684 1
2.0%
9577 1
2.0%
9547 1
2.0%
8946 1
2.0%
8857 1
2.0%
8764 1
2.0%
8746 1
2.0%

December
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7018.96
Minimum2324
Maximum20023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:25.410469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2324
5-th percentile2823.15
Q14237.25
median7225
Q38675.25
95-th percentile11887.35
Maximum20023
Range17699
Interquartile range (IQR)4438

Descriptive statistics

Standard deviation3444.2377
Coefficient of variation (CV)0.49070484
Kurtosis3.2977345
Mean7018.96
Median Absolute Deviation (MAD)2140.5
Skewness1.2901359
Sum350948
Variance11862773
MonotonicityNot monotonic
2024-02-19T21:54:25.605309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3458 2
 
4.0%
12903 1
 
2.0%
5254 1
 
2.0%
8476 1
 
2.0%
9674 1
 
2.0%
3478 1
 
2.0%
5563 1
 
2.0%
6532 1
 
2.0%
7463 1
 
2.0%
8673 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
2324 1
2.0%
2346 1
2.0%
2397 1
2.0%
3344 1
2.0%
3427 1
2.0%
3456 1
2.0%
3457 1
2.0%
3458 2
4.0%
3463 1
2.0%
3465 1
2.0%
ValueCountFrequency (%)
20023 1
2.0%
15887 1
2.0%
12903 1
2.0%
10646 1
2.0%
9872 1
2.0%
9674 1
2.0%
9567 1
2.0%
9546 1
2.0%
9535 1
2.0%
8856 1
2.0%

Total_sales
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)100.0%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean52726.511
Minimum33363
Maximum73931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2024-02-19T21:54:25.773802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33363
5-th percentile39812.5
Q144669.5
median52503
Q360802.5
95-th percentile68766.7
Maximum73931
Range40568
Interquartile range (IQR)16133

Descriptive statistics

Standard deviation9650.8049
Coefficient of variation (CV)0.18303515
Kurtosis-0.5942981
Mean52726.511
Median Absolute Deviation (MAD)8187
Skewness0.26994979
Sum2478146
Variance93138035
MonotonicityNot monotonic
2024-02-19T21:54:25.937219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
48826 1
 
2.0%
60156 1
 
2.0%
52503 1
 
2.0%
45023 1
 
2.0%
41227 1
 
2.0%
54688 1
 
2.0%
42566 1
 
2.0%
43036 1
 
2.0%
52586 1
 
2.0%
43634 1
 
2.0%
Other values (37) 37
74.0%
(Missing) 3
 
6.0%
ValueCountFrequency (%)
33363 1
2.0%
36429 1
2.0%
39481 1
2.0%
40586 1
2.0%
41227 1
2.0%
41936 1
2.0%
42566 1
2.0%
43036 1
2.0%
43634 1
2.0%
44029 1
2.0%
ValueCountFrequency (%)
73931 1
2.0%
71627 1
2.0%
69598 1
2.0%
66827 1
2.0%
65765 1
2.0%
64114 1
2.0%
64111 1
2.0%
63728 1
2.0%
63502 1
2.0%
63295 1
2.0%

Interactions

2024-02-19T21:54:16.241885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:48.878273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:51.049902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:53.302015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:55.494932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:57.749224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:59.850698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:01.862182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:03.894229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:05.841675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:07.722591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:09.701343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:12.125717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:14.024278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:16.430499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:48.993114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:51.178607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:53.466676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:55.627613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:57.860350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:59.967713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:01.963678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.077651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.006432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:07.852977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:09.849671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:12.272169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:14.209387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:16.621220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:49.145167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:51.344018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:53.590287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:55.761127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:58.006026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:00.116864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:02.079371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.193229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.170128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.026610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:10.032540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:12.373140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:14.358729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:16.786975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:49.329745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:51.509883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:53.780391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:56.077520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:58.173826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:00.229786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:02.220643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.347054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.295565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.183327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:10.486715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:12.509748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:14.497379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:16.918955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:49.497804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:51.651960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:53.885967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:56.177655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:58.347226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:00.328483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:02.541217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.453383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.400807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.340814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:10.658105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:12.672907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:14.628298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:17.057568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:49.645680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:51.862488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:54.042840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:56.323467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:58.469071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:00.512073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:02.706458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.587447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.561029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.446471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:10.791519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:12.874183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:14.805425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:17.257779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:49.811087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:52.030331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:54.178485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:56.492194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:58.626396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:00.659203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:02.838627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.719515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.682423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.588203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:10.944503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.027379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:14.997827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:17.429716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:49.900893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:52.132962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:54.327345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:56.646362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:58.760425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:00.781456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:02.960737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.818881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.791172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.679368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:11.102833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.134867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:15.120611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:17.556686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:50.030194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:52.312011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:54.520566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:56.794631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:58.878336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:00.939560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:03.099632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:04.951177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.894466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.827442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:11.261302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.263137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:15.280025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:17.703712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:50.199406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:52.527625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:54.660614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:56.952092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:59.002939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:01.077669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:03.234247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:05.067534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:06.991266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:08.976523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:11.408756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.392097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:15.444890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:17.892095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:50.472836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:52.663356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:54.861254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:57.072307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:59.146299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:01.187464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:03.382432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:05.198386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:07.116667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:09.108484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:11.550317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.530382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:15.637466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:18.055205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:50.627491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:52.831734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:55.044714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:57.227214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:59.293489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:01.342311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:03.560033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:05.360503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:07.259371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:09.240646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:11.678617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.661708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:15.790466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:18.168550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:50.721197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:52.976833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:55.161271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:57.376862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:59.479894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:01.514022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:03.662942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:05.527757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:07.412269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:09.355158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:11.860816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.745057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:15.922052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:18.372868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:50.858072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:53.114773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:55.281451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:57.549783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:53:59.633752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:01.642283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:03.780088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:05.707649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:07.540527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:09.552970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:11.983382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:13.892014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-19T21:54:16.055431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-19T21:54:26.118270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AprilAugustDecemberFebruaryIDJanuaryJulyJuneLast,FirstMarchMayNovemberOctoberPriceProduct NameSeptemberTotal_sales
April1.0000.0860.1620.1220.177-0.142-0.061-0.0940.000-0.182-0.1140.0580.0440.0000.0000.1410.015
August0.0861.0000.058-0.0120.1720.1340.015-0.0500.0000.0200.3950.2180.0500.1950.188-0.2140.591
December0.1620.0581.000-0.0880.021-0.0020.090-0.2080.000-0.2120.0260.126-0.2590.0000.0000.1890.252
February0.122-0.012-0.0881.0000.115-0.0780.410-0.0040.000-0.089-0.0020.2070.0600.1660.170-0.0490.239
ID0.1770.1720.0210.1151.0000.182-0.0190.1200.0000.153-0.2590.010-0.1820.0000.0000.019-0.099
January-0.1420.134-0.002-0.0780.1821.000-0.4980.1650.0000.0810.0010.210-0.2420.1780.156-0.0480.331
July-0.0610.0150.0900.410-0.019-0.4981.000-0.0460.066-0.1490.019-0.0660.2230.1230.2400.0110.045
June-0.094-0.050-0.208-0.0040.1200.165-0.0461.0000.0000.012-0.033-0.1160.0360.0000.0000.258-0.018
Last,First0.0000.0000.0000.0000.0000.0000.0660.0001.000-0.0440.080-0.056-0.1380.1170.0000.0400.054
March-0.1820.020-0.212-0.0890.1530.081-0.1490.012-0.0441.000-0.0920.011-0.0320.1470.1240.1040.177
May-0.1140.3950.026-0.002-0.2590.0010.019-0.0330.080-0.0921.0000.0930.0360.0000.0000.0780.519
November0.0580.2180.1260.2070.0100.210-0.066-0.116-0.0560.0110.0931.000-0.1270.0000.000-0.1380.563
October0.0440.050-0.2590.060-0.182-0.2420.2230.036-0.138-0.0320.036-0.1271.0000.0000.000-0.3740.171
Price0.0000.1950.0000.1660.0000.1780.1230.0000.1170.1470.0000.0000.0001.0000.9890.173-0.056
Product Name0.0000.1880.0000.1700.0000.1560.2400.0000.0000.1240.0000.0000.0000.9891.000-0.1530.079
September0.141-0.2140.189-0.0490.019-0.0480.0110.2580.0400.1040.078-0.138-0.3740.173-0.1531.000-0.096
Total_sales0.0150.5910.2520.239-0.0990.3310.045-0.0180.0540.1770.5190.5630.171-0.0560.079-0.0961.000

Missing values

2024-02-19T21:54:18.579343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-19T21:54:18.908176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-19T21:54:19.121554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDLast,FirstProduct NamePriceJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberTotal_sales
01Albertson, KathyLaptopaed200037993003400.01462.084921625218471827.010383984749491290348826.0
12Allenson, CarolCameraaed3000389950376589.01235.0103331578219872324.014339199367362002360156.0
23Altman, ZoeyComputeraed30004300106005389.01475.097871038213293497.0482863477746239752867.0
34Bittiman, WilliamMobileaed1000160037264986.01964.064821119317936325.0872285433257956748243.0
45Brennan, MichealCalculatoraed500890096393623.01369.098321893018533496.08462126648463345663295.0
56Allenson, CarolLaptopaed2000600012393247.01006.032031642913742732.048425688990788433363.0
67Altman, ZoeyCameraaed3000780054905430.01614.087421734910934598.0857585244564234650201.0
78David, ChloeComputeraed3000930062812347.01964.073031293713269743.0884625788445865557942.0
89Brennan, MichealMobileaed1000420075318732.01646.084631937414738623.0558896469957345763728.0
910Albertson, KathyCalculatoraed500170090182937.01643.0103781127319392345.01039955733558885647947.0
IDLast,FirstProduct NamePriceJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberTotal_sales
4041Flores, TiaLaptopaed2000544554793566.01546.045861265415348461.014235285798671064653987.0
4142Brennan, MichealCameraaed3000256345437564.01324.096741236616453941.0334687635435423749689.0
4243Jameson, RobinsonComputeraed3000865798784676.01235.043571865716734653.0983525456245537749296.0
4344Bittiman, WilliamMobileaed1000685434359676.01546.064671967613558564.0795413457895647653613.0
4445Altman, ZoeyCalculatoraed500457867999347.0NaN76451387416765347.097561043346434238NaN
4546Allenson, CarolLaptopaed2000967478563578.01846.064561754815437656.0356787539835856865765.0
4647Albertson, KathyCameraaed3000235568563748.02335.034581508718743563.07645128765347698749399.0
4748David, ChloeComputeraed3000856385339735.01867.078651298716478765.0664664638643954671627.0
4849Brennan, MichealMobileaed1000633453652477.01968.034651398713564353.0763425977345876944029.0
4950Counts, ElizabethCalculatoraed500864567859845.01224.07563196081653NaN10789386348677568NaN